The development of reliable interatomic potentials for large-scale molecular dynamics simulations of chemical processes at surfaces and interfaces is a formidable challenge because a wide range of atomic environments and very different types of bonding can be present. In recent years interatomic potentials based on artificial neural networks (NN) have emerged offering an unbiased approach to the construction of potential energy surfaces for systems that are difficult to describe by conventional potentials. Artrith, Hiller and Behler (pp. 1191–1203) review the basic properties of NN potentials and describe their construction for materials like metals and oxides. The accuracy and efficiency are demonstrated using copper and zinc oxide as benchmark systems. First results for a potential of the combined ternary CuZnO system aiming at the description of oxide-supported copper clusters are reported.